On embedding assessments of self-regulated learning into licensure activities in the health professions: a call to action
Bibliographic record
Abstract
How well have healthcare professionals and trainees been prepared for the inevitable demands for new learning that will arise in their future? Given the rapidity with which 'core healthcare knowledge' changes, medical educators have a responsibility to audit whether trainees have developed the capacity to effectively self-regulate their learning. Trainees who engage in effective self-regulated learning (SRL) skillfully monitor and control their cognition, motivation, behaviour, and environment to adaptively meet demands for new learning. However, medical curricula rarely assess trainees' capacity to engage in these strategic processes. In this position paper, we argue for a paradigm shift toward assessing SRL more deliberately in undergraduate and postgraduate programs, as well as in associated licensing activities. Specifically, we explore evidence supporting an innovative blend of principles from the science on SRL, and on preparation for future learning (PFL) assessments. We propose recommendations for how program designers, curriculum developers, and assessment leads in undergraduate and postgraduate training programs, and in licensing bodies can work together to develop integrated assessments that measure how and how well trainees engage in SRL. Claims about lifelong learning in health professions education have gone unmatched by responsive curricular changes for far too long. Further neglecting these important competencies represents a disservice to medical trainees and a potential risk to the future patients they will care for.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.283 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".